Graph Neural Networks (GNNs) have recently achieved significant success in processing non-Euclidean datasets, such as social and protein-protein interaction networks. However, these datasets often ...

To address these challenges, this paper proposes the Graph-based MultiScale Stacked Autoencoder (GMScaleSAE) framework. GMScaleSAE integrates a graph structure learning module to derive an adjacency ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...